SoroushVahidi/scidocs huggingface dataset:
A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation
(2017)

In this paper, we adopt 3D CNNs to segment the pancreas in CT images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D applications due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarseto-fine framework for volumetric pancreas segmentation to tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the state-of-the-art in terms of Dice-Sørensen Coefficient (DSC). Moreover, the worst case of DSC on the NIH dataset was improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.
Changes in excitability of the cortical projections to the human tibialis anterior after paired associative stimulation.
(2007)

Paired associative stimulation (PAS) based on Hebb's law of association can induce plastic changes in the intact human. The optimal interstimulus interval (ISI) between the peripheral nerve and transcranial magnetic stimulus is not known for muscles of the lower leg. The aims of this study were to investigate the effect of PAS for a variety of ISIs and to explore the efficacy of PAS when applied during dynamic activation of the target muscle. PAS was applied at 0.2 Hz for 30 min with the tibialis anterior (TA) at rest. The ISI was varied randomly in seven sessions (n = 5). Subsequently, PAS was applied (n = 14, ISI = 55 ms) with the TA relaxed or dorsi-flexing. Finally, an optimized ISI based on the subject somatosensory evoked potential (SEP) latency plus a central processing delay (6 ms) was used (n = 13). Motor-evoked potentials (MEPs) were elicited in the TA before and after the intervention, and the size of the TA MEP was extracted. ISIs of 45, 50, and 55 ms increased and 40 ms decreased TA MEP significantly (P = 0.01). PAS during dorsi-flexion increased TA MEP size by 92% (P = 0.001). PAS delivered at rest resulted in a nonsignificant increase; however, when the ISI was optimized from SEP latency recordings, all subjects showed significant increases (P = 0.002). No changes in MEP size occurred in the antagonist. Results confirm that the excitability of the corticospinal projections to the TA but not the antagonist can be increased after PAS. This is strongly dependent on the individualized ISI and on the activation state of the muscle.
Characterization of Polymeric Microneedle Arrays for Transdermal Drug Delivery
(2013)

Microfabrication of dissolvable, swellable, and biodegradable polymeric microneedle arrays (MNs) were extensively investigated based in a nano sensitive fabrication style known as micromilling that is then combined with conventional micromolding technique. The aim of this study was to describe the polymer selection, and optimize formulation compounding parameters for various polymeric MNs. Inverse replication of micromilled master MNs reproduced with polydimethylsiloxane (PDMS), where solid out of plane polymeric MNs were subsequently assembled, and physicochemically characterized. Dissolvable, swellable, and biodegradable MNs were constructed to depth of less than 1 mm with an aspect ratio of 3.6, and 1/2 mm of both inter needle tip and base spacing. Micromolding step also enabled to replicate the MNs very precisely and accurate. Polymeric microneedles (MN) precision was ranging from ± 0.18 to ± 1.82% for microneedle height, ± 0.45 to ± 1.42% for base diameter, and ± 0.22 to ± 0.95% for interbase spacing. Although dissolvable sodium alginate MN showed less physical robustness than biodegradable polylactic-co-glycolic acid MN, their thermogravimetric analysis is of promise for constructing these polymeric types of matrix devices.
Mobile robot navigation based on lidar
(2018)

A wheeled mobile robot based on Arduino was described in this article. Hardware selection, Simultaneous Localization and Mapping (SLAM), Path Planning and the principle of the algorithm applied in this process are introduced in detail. The algorithm used in ROS is further verified by experiments. In order to verify the feasibility of two different 2D SLAM algorithms, we use the wheeled robot built in the lab to do the experiment in the same indoor environment and compare their results.
Analysis and modelling of a bidirectional push-pull converter with PWM plus phase-shift control
(2016)

This paper presents analysis and modeling for a bidirectional push-pull dc-dc converter. In particular, attention is paid to leakage current reduction with PWM plus phase-shift control. The leakage current in different operating modes has been compared to identify the efficient modes for various operation conditions. The state space averaging method is adopted to derive the small signal and large signal models of the most efficient mode. A 30~70V/300V prototype was built. Both simulation results and experimental results prove the validity of the model.
SystemT: a system for declarative information extraction
(2008)

As applications within and outside the enterprise encounter increasing volumes of unstructured data, there has been renewed interest in the area of information extraction (IE) -- the discipline concerned with extracting structured information from unstructured text. Classical IE techniques developed by the NLP community were based on cascading grammars and regular expressions. However, due to the inherent limitations of grammarbased extraction, these techniques are unable to: (i) scale to large data sets, and (ii) support the expressivity requirements of complex information tasks. At the IBM Almaden Research Center, we are developing SystemT, an IE system that addresses these limitations by adopting an algebraic approach. By leveraging well-understood database concepts such as declarative queries and costbased optimization, SystemT enables scalable execution of complex information extraction tasks. In this paper, we motivate the SystemT approach to information extraction. We describe our extraction algebra and demonstrate the effectiveness of our optimization techniques in providing orders of magnitude reduction in the running time of complex extraction tasks.
A Fractional-N Sub-Sampling PLL using a Pipelined Phase-Interpolator With an FoM of -250 dB
(2016)

A fractional-N sub-sampling PLL architecture based on pipelined phase-interpolator and Digital-to-Time-Converter (DTC) is presented in this paper. The combination of pipelined phase-interpolator and DTC enables efficient design of the multi-phase generation mechanism required for the fractional operation. This technique can be used for designing a fractional-N PLL with low in-band phase noise and low spurious tones with low power consumption. The short-current-free pipelined phase-interpolator used in this work is capable of achieving high-linearity with low-power while minimizing the intrinsic jitter. A number of other circuit techniques and layout techniques are also employed in this design for ensuring high-performance operation with minimal chip area and power consumption. The proposed fractional-N PLL is implemented in standard 65 nm CMOS technology. The PLL has an operating range of 600 MHz from 4.34 GHz to 4.94 GHz. In fractional-N mode, the proposed PLL achieves -249.5 dB FoM and less than -59 dBc fractional spurs.
Structural Design of Convolutional Neural Networks for Steganalysis
(2016)

Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent (TanH) at early stages of the networks and reduce the strength of modeling using 1×1 convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.
Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection
(2018)

In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim. It consists of identifying the set of sentences, out of a long text, deemed capable of being factchecked. This paper is a collaborative work between Full Fact, an independent factchecking charity, and academic partners. Leveraging the expertise of professional factcheckers, we develop an annotation schema and a benchmark for automated claim detection that is more consistent across time, topics and annotators than previous approaches. Our annotation schema has been used to crowdsource the annotation of a dataset with sentences from UK political TV shows. We introduce an approach based on universal sentence representations to perform the classification, achieving an F1 score of 0.83, with over 5% relative improvement over the state-of-the-art methods ClaimBuster and ClaimRank. The system was deployed in production and received positive user feedback.
A Low power and area efficient CLA adder design using Full swing GDI technique
(2015)

The low power VLSI design has an important role in designing of many electronic systems. While designing any combinational or sequential circuits, the important parameters like power consumption, implementation area, voltage leakage and performance of the circuit are to be considered. Design of area, high speed and powerefficient data path logic systems forms the largest areas of research in VLSI system design. This paper presents a low power Carry look ahead adder design using Full swing Gate diffusion (FS-GDI) technique. The proposed CLA implementation utilizes improved full-swing GDI F1 and F2 gates, which are the counterparts of standard CMOS NAND and NOR gates. The basic Gate Diffusion Input (GDI) logic style suffers from some practical limitations like swing degradation, fabrication complexity in standard CMOS process and bulk connections. These limitations can be overcome by Full swing GDI technique. The proposed technique utilizes a single swing restoration (SR) transistor to improve the output swing of F1 and F2 GDI gates. A 16-bit CLA is designed and Simulations are performed by Mentor graphics 130nm CMOS technology ELDO simulator. Simulation results have shown a greater reduction in delay, power dissipation and area.
A 300 nW , 15 ppm / C , 20 ppm / V CMOS Voltage Reference Circuit Consisting of Subthreshold MOSFETs
(2009)

A low-power CMOS voltage reference was developed using a 0.35 m standard CMOS process technology. The device consists of MOSFET circuits operated in the subthreshold region and uses no resistors. It generates two voltages having opposite temperature coefficients and adds them to produce an output voltage with a near-zero temperature coefficient. The resulting voltage is equal to the extrapolated threshold voltage of a MOSFET at absolute zero temperature, which was about 745 mV for the MOSFETs we used. The temperature coefficient of the voltage was 7 ppm/ C at best and 15 ppm/ C on average, in a range from 20 to 80 C. The line sensitivity was 20 ppm/V in a supply voltage range of 1.4–3 V, and the power supply rejection ratio (PSRR) was 45 dB at 100 Hz. The power dissipation was 0.3 W at 80 C. The chip area was 0.05 mm . Our device would be suitable for use in subthreshold-operated, power-aware LSIs.
Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce
(2017)

In this paper, we present a unied end-to-end approach to build a large scale Visual Search and Recommendation system for ecommerce. Previous works have targeted these problems in isolation. We believe a more eective and elegant solution could be obtained by tackling them together. We propose a unied Deep Convolutional Neural Network architecture, called VisNet 1, to learn embeddings to capture the notion of visual similarity, across several semantic granularities. We demonstrate the superiority of our approach for the task of image retrieval, by comparing against the state-of-the-art on the Exact Street2Shop [14] dataset. We then share the design decisions and trade-os made while deploying the model to power Visual Recommendations across a catalog of 50M products, supporting 2K queries a second at Flipkart, India’s largest e-commerce company. e deployment of our solution has yielded a signicant business impact, as measured by the conversion-rate.
CMOS Amperometric Instrumentation and Packaging for Biosensor Array Applications
(2011)

An integrated CMOS amperometric instrument with on-chip electrodes and packaging for biosensor arrays is presented. The mixed-signal integrated circuit supports a variety of electrochemical measurement techniques including linear sweep, constant potential, cyclic and pulse voltammetry. Implemented in 0.5 μm CMOS, the 3 × mm2 chip dissipates 22.5 mW for a 200 kHz clock. The highly programmable chip provides a wide range of user-controlled stimulus rate and amplitude settings with a maximum scan range of 2 V and scan rates between 1 mV/sec and 400 V/sec. The amperometric readout circuit provides ±500 fA linear resolution and supports inputs up to ±47 μA. A 2 × 2 gold electrode array was fabricated on the surface of the CMOS instrumentation chip. An all-parylene packaging scheme was developed for compatibility with liquid test environments as well as a harsh piranha electrode cleaning process. The chip was tested using cyclic voltammetry of different concentrations of potassium ferricyanide at 100 mV/s and 200 mV/s, and results were identical to measurements using commercial instruments.
Performance Enhancement of Boost Converter Based on PID Controller Plus Linear-to-Nonlinear Translator
(2010)

Here, A PID controller plus a novel linear-to-nonlinear translator is proposed and applied to the boost converter to improve the operating performance of the boost converter under large transient variations in load all over the quiescent dc input voltage range. The proposed translator is inserted between the PID controller and the main power stage such that the PID controller considers the behavior of the boost converter to be quasi-linear, thereby suppressing the effect of nonlinear behavior of the boost converter on the PID controller. Besides, variations in dc gain of the control-to-output transfer function with the translator are smaller than those without the translator, thus making system control easy to attain. Also, one-comparator counter-based sampling without any A-D converter is presented herein to realize the proposed control strategy. As mentioned above, the proposed control strategy can make the boost converter stably operated under large load transient responses all over the quiescent dc input voltage range.
A five level cascaded H-bridge inverter based on space vector pulse width modulation technique
(2014)

Multilevel inverters brought a tremendous revolution in high power superior performance industrial drive applications. Minimizing harmonic distortion is a challenging issue for power electronic drive. This problem can be solved by developing suitable modulation technique. There are number of modulation techniques established in the last decade. Among them, the space vector pulse width modulation (SVPWM) is extensively used because of their easier digital realization, better DC bus utilization and lower THD. Conventional SVPWM requires different number of logical computations of on time calculations and different number of lookup table for each triangle due to having their different number of switching states. In this paper, a simple SVPWM is presented where proposed only four logical computations of on time calculation, four active switching states and four lookup table for each triangle. Five level cascaded h-bridge inverter (CHBI) based on SVPWM technique are modeled and analyzed by using MATLAB/Simulink and origin 6.1 with a passive R-L load that can be extended to any level. These results are compared with other lower levels of inverter and with previous established results. From these results, five level CHBI based on SVPWM shows better performance compared to others in terms of THD.
TA-COS 2018 : 2 nd Workshop on Text Analytics for Cybersecurity and Online Safety
(2018)

In this study, we present toxicity annotation for a Thai Twitter Corpus as a preliminary exploration for toxicity analysis in the Thai language. We construct a Thai toxic word dictionary and select 3,300 tweets for annotation using the 44 keywords from our dictionary. We obtained 2,027 and 1,273 toxic and non-toxic tweets, respectively; these were labeled by three annotators. The result of corpus analysis indicates that tweets that include toxic words are not always toxic. Further, it is more likely that a tweet is toxic, if it contains toxic words indicating their original meaning. Moreover, disagreements in annotation are primarily because of sarcasm, unclear existing target, and word sense ambiguity. Finally, we conducted supervised classification using our corpus as a dataset and obtained an accuracy of 0.80, which is comparable with the inter-annotator agreement of this dataset. Our dataset is available on GitHub.
Correlation Hashing Network for Efficient Cross-Modal Retrieval
(2016)

Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which improves the quality of hash coding by exploiting the semantic correlation across different modalities, has received increasing attention recently. For most existing cross-modal hashing methods, an object is first represented as of vector of hand-crafted or machine-learned features, followed by another separate quantization step that generates binary codes. However, suboptimal hash coding may be produced, because the quantization error is not statistically minimized and the feature representation is not optimally compatible with the binary coding. In this paper, we propose a novel Correlation Hashing Network (CHN) architecture for cross-modal hashing, in which we jointly learn good data representation tailored to hash coding and formally control the quantization error. The CHN model is a hybrid deep architecture constituting four key components: (1) an image network with multiple convolution-pooling layers to extract good image representations, and a text network with several fully-connected layers to extract good text representations; (2) a fully-connected hashing layer to generate modality-specific compact hash codes; (3) a squared cosine loss layer for capturing both cross-modal correlation and within-modal correlation; and (4) a new cosine quantization loss for controlling the quality of the binarized hash codes. Extensive experiments on standard cross-modal retrieval datasets show the proposed CHN model yields substantial boosts over latest state-of-the-art hashing methods.
ontoAGA: Ontology to Support Educational Systems Interoperability
(2016)

Looking forward to promote the exchange and integration of information in order to build a unique reference model for the universities it is essencial to assure commom concepts of higher education institutions in Brazil. This paper proposes an University Support Management Ontology, named ontoAGA, to help the operation of legacy data in Semantic Web in a standardized manner and to allow the integration of databases with different technologies.To evaluate the research questions two usages cenarios were defined and the results point to the proposal viability. Resumo. Para promover o intercâmbio e a integração de informações de forma a se construir um modelo de referência para as universidades há necessidade de se estruturar conceitos comuns às instituições de ensino superior no Brasil. Este artigo propõe uma ontologia, no domínio da gestão acadêmica nas universidades, denominada ontoAGA, para ajudar a exploração de dados legados na web Semântica de forma padronizada e para possibilitar a integração de bases de dados de diferentes tecnologias. Para avaliação das questões de pesquisa foram definidos dois cenários de uso, cujos resultados apontam para a viabilidade da proposta.
Safer smart contracts through type-driven development
(2016)

We show how dependent and polymorphic types can make smart contract development safer. This is demonstrated by using the functional language Idris to describe smart contracts on the Ethereum platform. In particular, we show how one class of common errors can be captured at compile time using dependent types and algebraic side effects. We also bring type annotations to the realm of smart contracts, helping developers to circumvent another class of common errors. To demonstrate the feasibility of our solutions, we have extended the Idris compiler with a backend for the Ethereum Virtual Machine. While we find that the functional paradigm might not be the most suitable for the domain, our approach solves the identified problems and provides advantages over the languages in current use.
Complexities in Internet peering: Understanding the “Black” in the “Black Art”
(2015)

Peering in the Internet interdomain network has long been considered a “black art”, understood in-depth only by a select few peering experts while the majority of the network operator community only scratches the surface employing conventional rules-of-thumb to form peering links through ad hoc personal interactions. Why is peering considered a black art? What are the main sources of complexity in identifying potential peers, negotiating a stable peering relationship, and utility optimization through peering? How do contemporary operational practices approach these problems? In this work we address these questions for Tier-2 Network Service Providers. We identify and explore three major sources of complexity in peering: (a) inability to predict traffic flows prior to link formation (b) inability to predict economic utility owing to a complex transit and peering pricing structure (c) computational infeasibility of identifying the optimal set of peers because of the network structure. We show that framing optimal peer selection as a formal optimization problem and solving it is rendered infeasible by the nature of these problems. Our results for traffic complexity show that 15% NSPs lose some fraction of customer traffic after peering. Additionally, our results for economic complexity show that 15% NSPs lose utility after peering, approximately, 50% NSPs end up with higher cumulative costs with peering than transit only, and only 10% NSPs get paid-peering customers.